This paper presents an approach to detect stationary foreground objects in naturally busy surveillance video scenes with several moving objects. Our approach is inspired by human's visual cognition processes and builds upon a multi-tier video tracking paradigm with main layers being the spatially based "peripheral tracking" loosely corresponding to the peripheral vision and the object based "vision tunnels " for focused attention and analysis of tracked objects. Humans allocate their attention to different aspects of objects and scenes based on a defined task. In our model, a specific processing layer corresponding to allocation of attention is used for detection of objects that become stationary. The static object layer, a natural extension of this framework, detects and maintains the stationary foreground objects by using the moving object and scene information from Peripheral Tracker and the Scene Description layers. Simple event detection modules then use the enduring stationary objects to determine events such as Parked Vehicles or Abandoned Bags.